Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Abstract State Machines: A Method for High-Level System Design and Analysis
Abstract State Machines: A Method for High-Level System Design and Analysis
A Thorough Axiomatization of a Principle of Conditional Preservation in Belief Revision
Annals of Mathematics and Artificial Intelligence
Theoretical Computer Science - Formal methods for components and objects
IJCAR '08 Proceedings of the 4th international joint conference on Automated Reasoning
Modeling belief in dynamic systems part II: revision and update
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Modelling conditional knowledge discovery and belief revision by abstract state machines
ASM'03 Proceedings of the abstract state machines 10th international conference on Advances in theory and practice
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
Conditionals in nonmonotonic reasoning and belief revision: considering conditionals as agents
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Belief revision is a key functionality for any intelligent agent being able to perceive pieces of knowledge from its environment and to give back sentences she believes to be true with a certain degree of belief. We report on a refinement of a previous, abstract ASM specification of Condor, a system modeling such an agent, to a fully operational specification implemented in AsmL. The complete AsmL implementation of various belief revision operators is presented, demonstrating how using AsmL enabled a high-level implementation that minimizes the gap between the abstract specification of the underlying concepts and the executable code in the implemented system. Based on ASM refinement and verification concepts, a full mathematical correctness proof for different belief revision operators realized in Condor@AsmL is given.